Experimental indoors localization using distance measurements obtained from visual data

In recent years, the emergence of artificial intelligence increases the demand of automatic and robust localization outdoors and indoors. While GPS provides enough accuracy in most outdoor cases, there is still a lack of robust and efficient indoor localization systems available on the market. In this report, an experimental framework for indoor localization is developed and tested. To operate an automatic robot owned by LCAV laboratory, two different operation modes have been successfully implemented, including controlling a robot in real-time or with extra input containing a list of commands. Also, a new visual fiducial system have been developed, and is able to capture the locations of Apriltags inside a room accurately. Multidimensional scaling (MDS) and Squared range least square (SRLS) algorithms containing distance information will also be introduced and the final localization result is within 2% error of tolerance compared with the ground truth results measured by a laser meter.

Hybrid, metric - topological, mobile robot navigation

Nicola Tomatis

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested.

EPFL2001

Topological SLAM

Adriana Tapus

This thesis is about topological navigation, more precisely about space representation, perception, localization and mapping. All these elements are needed in order to obtain a robust and reliable framework for navigation. This is essential in order to move in an environment, manipulate objects in it and avoid collisions. The method proposed in this dissertation is suitable for fully autonomous mobile robots, operating in structured indoor and outdoor environments. High robustness is necessary to obtain a distinctive and reliable representation of the environment. This can be obtained by combining the information acquired by several sensors with complementary characteristics. A multimodal perception system that includes the wheel encoders for odometry and two extereoceptive sensors composed of a laser range finder that gives a 360° view of the environment and an omnidirectional camera are used in this work. Significant, robust and stable features are extracted from sensory data. The different features extracted from the extereoceptive sensors (i.e. corners, vertical edges, color patches, etc.) are fused and combined into a single, circular, and distinctive space representation named the fingerprint of a place. This representation is adapted for topological navigation and is the foundation for the whole dissertation. One of the major tasks of a mobile robot is localization. Different topological localization approaches based on the fingerprint concept are presented in this dissertation. Localization on a fingerprint-based representation is reduced to a problem of fingerprint matching. Two of these methods make use of the Bayesian Programming (BP) formalism and two others are based on dynamic programming. They also show how multimodal perception increases the reliability of topological localization for mobile robots. In order to autonomously acquire and create maps, robots have to explore their environment. Several exploration tools for indoor environments are presented: wall following, mid-line following, center of free space of a room, door detection, and environment structure identification. An automatic and incremental topological mapping system based on fingerprints of places and a global localizer using Partially Observable Markov Decision Processes (POMDP) are proposed. The construction of a topological mapping system is combined with localization, both relying on fingerprints of places, in order to perform Simultaneous Localization and Mapping (SLAM). This enables navigation of an autonomous mobile robot in a structured environment without relying on maps given a priori, without using artificial landmarks and by employing a semantic spatial representation that allows a more natural interface between humans and robots. The fingerprint approach, combining the information from all sensors available to the robot, reduces perceptual aliasing and improves the distinctiveness of places. This fingerprint-based approach yields a consistent and distinctive representation of the environment and is extensible in that it permits spatial cognition beyond just pure navigation. All these methodologies have been validated through experiments. Indoor and outdoor experiments have been conducted over a distance exceeding 2 km. The fingerprints of places proved to provide a compact and distinctive methodology for space representation and place recognition – they permit encoding of a huge amount of placerelated information in a single circular sequence of features. The experiments have verified the efficacy and reliability of this approach.

EPFL2005

Enactive robot vision

Mototaka Suzuki

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007